This Notebook is a continuation of DCGAN.ipynb
DCGAN.ipynb > CGAN.ipynb > WGAN.ipynb
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
!nvidia-smi
Sat Feb 4 19:36:45 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA A100-SXM... Off | 00000000:00:04.0 Off | 0 |
| N/A 31C P0 52W / 400W | 0MiB / 40960MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+

A GAN contains a generator and a discriminator. A discriminator can be like a Convolutional Neural Network that tries to determine whether an image is real or fake. While doing so, the discriminator leaves behind a gradient that the generator can use to improve its own output. The generator competes with the discriminator and generates images. However when exploring simple GANs and looking at the output I noticed that the images was difficult to interpret or it seemed like the images was a little bit of an airplane and a little bit of a car.

CGAN is different from GAN where not only the pixels of real images are passed into the model , labels of each image is also passed in . As such the generator model generates images with a specific label CDCGAN contains additional Convolutional transpose layers in the generator and Convolutional layers in the Discriminator
image by Saul Dobilas https://towardsdatascience.com/cgan-conditional-generative-adversarial-network-how-to-gain-control-over-gan-outputs-b30620bd0cc8
The Generator and discriminator of the CGANs are conditioned on additional information such as class labels.
| Class Number | Name |
|---|---|
| 0 | Airplane |
| 1 | Automobile |
| 2 | Bird |
| 3 | Cat |
| 4 | Deer |
| 5 | Dog |
| 6 | Frog |
| 7 | Horse |
| 8 | Ship |
| 9 | Truck |
# DL modules
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import *
from tensorflow.keras import layers
from tensorflow.keras import Sequential, Model
import keras.backend as K
from tensorflow.keras import initializers
! pip install tensorflow_addons
from tensorflow_addons.layers import SpectralNormalization
from tensorflow.keras.optimizers import Adam
# relevent libraries
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
## matplotlib stylings
plt.rcParams['figure.figsize'] = 12, 8
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting tensorflow_addons
Downloading tensorflow_addons-0.19.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.1/1.1 MB 21.1 MB/s eta 0:00:00
Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.8/dist-packages (from tensorflow_addons) (2.7.1)
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from tensorflow_addons) (23.0)
Installing collected packages: tensorflow_addons
Successfully installed tensorflow_addons-0.19.0
batch_size = 128
num_channels = 3
num_classes = 10
image_size = 32
latent_dim = 128
# We'll use all the available examples from both the training and test
# sets.
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
all_digits = np.concatenate([x_train, x_test])
all_labels = np.concatenate([y_train, y_test])
# Scale the pixel values to [0, 1] range, add a channel dimension to
# the images, and one-hot encode the labels.
all_digits = all_digits.astype("float32") / 127.5 - 1
all_digits = np.reshape(all_digits, (-1, 32, 32, 3))
all_labels = keras.utils.to_categorical(all_labels, 10)
# Create tf.data.Dataset.
dataset = tf.data.Dataset.from_tensor_slices((all_digits, all_labels))
dataset = dataset.shuffle(buffer_size=1024).batch(batch_size)
print(f"Shape of training images: {all_digits.shape}")
print(f"Shape of training labels: {all_labels.shape}")
Shape of training images: (60000, 32, 32, 3) Shape of training labels: (60000, 10)
# plot some images
plt.figure(figsize=(10, 10))
for i in range(25):
plt.subplot(5, 5, i + 1)
plt.imshow((all_digits[i] + 1) / 2)
plt.axis("off")
plt.show()
We try to add the latent dimensions and the number of classes in the image so that we can condition the generator to be able to generate images based on classes. This is also true for the discriminator such that the discriminator that distinguish whether an image is fake and also the class of it.
generator_in_channels = latent_dim + num_classes
discriminator_in_channels = num_channels + num_classes
print(generator_in_channels, discriminator_in_channels)
138 13
# Create the discriminator.
discriminator = keras.Sequential(
[
keras.layers.InputLayer((32, 32, discriminator_in_channels)),
SpectralNormalization(
layers.Conv2D(32, kernel_size=4 , strides=2, padding="same"),
),
layers.LeakyReLU(alpha=0.2),
SpectralNormalization(
layers.Conv2D(64, kernel_size=4 , strides=2, padding="same"),
),
layers.LeakyReLU(alpha=0.2),
SpectralNormalization(
layers.Conv2D(128, kernel_size=4 , strides=2, padding="same"),
),
SpectralNormalization(
layers.Conv2D(256, kernel_size=4 , strides=2, padding="same"),
),
layers.LeakyReLU(alpha=0.2),
layers.GlobalMaxPooling2D(),
layers.Dense(1, activation='sigmoid'),
],
name="discriminator",
)
discriminator.summary()
Model: "discriminator"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
spectral_normalization_15 ( (None, 16, 16, 32) 6720
SpectralNormalization)
leaky_re_lu_15 (LeakyReLU) (None, 16, 16, 32) 0
spectral_normalization_16 ( (None, 8, 8, 64) 32896
SpectralNormalization)
leaky_re_lu_16 (LeakyReLU) (None, 8, 8, 64) 0
spectral_normalization_17 ( (None, 4, 4, 128) 131328
SpectralNormalization)
spectral_normalization_18 ( (None, 2, 2, 256) 524800
SpectralNormalization)
leaky_re_lu_17 (LeakyReLU) (None, 2, 2, 256) 0
global_max_pooling2d_4 (Glo (None, 256) 0
balMaxPooling2D)
dense_15 (Dense) (None, 1) 257
=================================================================
Total params: 696,001
Trainable params: 695,521
Non-trainable params: 480
_________________________________________________________________
# Create the generator.
generator = keras.Sequential(
[
keras.layers.InputLayer((generator_in_channels,)),
# We want to generate 128 + num_classes coefficients to reshape into a
# 7x7x(128 + num_classes) map.
layers.Dense(8 * 8 * generator_in_channels),
layers.LeakyReLU(alpha=0.2),
layers.Reshape((8, 8, generator_in_channels)),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(3, (8, 8), padding="same", activation="tanh"),
],
name="generator",
)
generator.summary()
Model: "generator"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_14 (Dense) (None, 8832) 1227648
leaky_re_lu_12 (LeakyReLU) (None, 8832) 0
reshape_10 (Reshape) (None, 8, 8, 138) 0
conv2d_transpose_38 (Conv2D (None, 16, 16, 128) 282752
Transpose)
leaky_re_lu_13 (LeakyReLU) (None, 16, 16, 128) 0
conv2d_transpose_39 (Conv2D (None, 32, 32, 128) 262272
Transpose)
leaky_re_lu_14 (LeakyReLU) (None, 32, 32, 128) 0
conv2d_25 (Conv2D) (None, 32, 32, 3) 24579
=================================================================
Total params: 1,797,251
Trainable params: 1,797,251
Non-trainable params: 0
_________________________________________________________________
class ConditionalGAN(keras.Model):
def __init__(self, discriminator, generator, latent_dim):
super().__init__()
self.discriminator = discriminator
self.generator = generator
self.latent_dim = latent_dim
self.gen_loss_tracker = keras.metrics.Mean(name="generator_loss")
self.disc_loss_tracker = keras.metrics.Mean(name="discriminator_loss")
@property
def metrics(self):
return [self.gen_loss_tracker, self.disc_loss_tracker]
def compile(self, d_optimizer, g_optimizer, loss_fn):
super().compile()
self.d_optimizer = d_optimizer
self.g_optimizer = g_optimizer
self.loss_fn = loss_fn
def train_step(self, data):
# Unpack the data.
real_images, one_hot_labels = data
# Add dummy dimensions to the labels so that they can be concatenated with
# the images. This is for the discriminator.
image_one_hot_labels = one_hot_labels[:, :, None, None]
image_one_hot_labels = tf.repeat(
image_one_hot_labels, repeats=[image_size * image_size]
)
image_one_hot_labels = tf.reshape(
image_one_hot_labels, (-1, image_size, image_size, num_classes)
)
# Sample random points in the latent space and concatenate the labels.
# This is for the generator.
batch_size = tf.shape(real_images)[0]
random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
random_vector_labels = tf.concat(
[random_latent_vectors, one_hot_labels], axis=1
)
# Decode the noise (guided by labels) to fake images.
generated_images = self.generator(random_vector_labels)
# Combine them with real images. Note that we are concatenating the labels
# with these images here.
fake_image_and_labels = tf.concat([generated_images, image_one_hot_labels], -1)
real_image_and_labels = tf.concat([real_images, image_one_hot_labels], -1)
combined_images = tf.concat(
[fake_image_and_labels, real_image_and_labels], axis=0
)
# Assemble labels discriminating real from fake images.
labels = tf.concat(
[tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0
)
# Train the discriminator.
with tf.GradientTape() as tape:
predictions = self.discriminator(combined_images)
d_loss = self.loss_fn(labels, predictions)
grads = tape.gradient(d_loss, self.discriminator.trainable_weights)
self.d_optimizer.apply_gradients(
zip(grads, self.discriminator.trainable_weights)
)
# Sample random points in the latent space.
random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
random_vector_labels = tf.concat(
[random_latent_vectors, one_hot_labels], axis=1
)
# Assemble labels that say "all real images".
misleading_labels = tf.zeros((batch_size, 1))
# Train the generator (note that we should *not* update the weights
# of the discriminator)!
with tf.GradientTape() as tape:
fake_images = self.generator(random_vector_labels)
fake_image_and_labels = tf.concat([fake_images, image_one_hot_labels], -1)
predictions = self.discriminator(fake_image_and_labels)
g_loss = self.loss_fn(misleading_labels, predictions)
grads = tape.gradient(g_loss, self.generator.trainable_weights)
self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))
# Monitor loss.
self.gen_loss_tracker.update_state(g_loss)
self.disc_loss_tracker.update_state(d_loss)
return {
"g_loss": self.gen_loss_tracker.result(),
"d_loss": self.disc_loss_tracker.result(),
}
class CONDGANMonitor(keras.callbacks.Callback):
def __init__(self, num_img=100, latent_dim=128,generator=0):
self.num_img = num_img
self.latent_dim = latent_dim
self.vmin = 0
self.vmax = 1
def on_epoch_end(self, epoch, logs=None):
class_labels = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
if epoch % 50 == 0:
# Save the model weights
tf.keras.models.save_model(generator, '/content/drive/MyDrive/Colab Notebooks/DELE_CA2/Models/DCGAN/final_DCGANgen.h5')
# self.model.save_weights(f"/content/drive/MyDrive/Colab Notebooks/DELE_CA2/Models/CGAN/finalmodel_weights_epoch_{epoch + 1}.h5")
# Plot the generated images
num_classes = 10
fig, axs = plt.subplots(num_classes, 10, figsize=(10, num_classes))
for i in range(num_classes):
class_label = keras.utils.to_categorical([i], num_classes)
class_label = tf.cast(class_label, tf.float32)
# Generate 10 random noise vectors
random_noise = tf.random.normal(shape=(10, self.latent_dim))
# Repeat the class label for each noise vector
class_label = tf.repeat(class_label, repeats=10, axis=0)
# Concatenate the noise and class label
noise_and_label = tf.concat([random_noise, class_label], axis=1)
# Run inference with the generator
fake_images = self.model.generator(noise_and_label)
fake_images -= 1
fake_images /= 1
# fake_images = tf.image.convert_image_dtype(fake_images, dtype=tf.float32, saturate=True)
# Plot the generated images
for j in range(10):
img = keras.preprocessing.image.array_to_img(fake_images[j])
axs[i, j].imshow(img)
axs[i, j].set_title(class_labels[i])
axs[i, j].axis("off")
plt.show()
logit is a type of function that maps probability from (0,1) to ( - inifinity , inifinity ) since I am using Sigmoid activation for Discriminator i will be setting from_logits to False
cond_gan = ConditionalGAN(
discriminator=discriminator, generator=generator, latent_dim=128
)
cond_gan.compile(
d_optimizer=keras.optimizers.Adam(learning_rate=0.0003),
g_optimizer=keras.optimizers.Adam(learning_rate=0.0003),
loss_fn=keras.losses.BinaryCrossentropy(from_logits=False),
)
con_hist = cond_gan.fit(dataset, epochs=500, callbacks=[CONDGANMonitor(num_img=100, latent_dim=128)])
Epoch 1/500 469/469 [==============================] - 32s 65ms/step - g_loss: 2.5748 - d_loss: 0.7390 Epoch 2/500 468/469 [============================>.] - ETA: 0s - g_loss: 3.2424 - d_loss: 0.4489
469/469 [==============================] - 31s 66ms/step - g_loss: 3.2433 - d_loss: 0.4490 Epoch 3/500 469/469 [==============================] - 30s 63ms/step - g_loss: 1.5572 - d_loss: 0.5313 Epoch 4/500 469/469 [==============================] - 30s 64ms/step - g_loss: 2.1542 - d_loss: 0.4961 Epoch 5/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.6244 - d_loss: 0.5708 Epoch 6/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.6355 - d_loss: 0.5787 Epoch 7/500 469/469 [==============================] - 31s 65ms/step - g_loss: 1.8072 - d_loss: 0.7063 Epoch 8/500 469/469 [==============================] - 31s 65ms/step - g_loss: 1.8938 - d_loss: 0.5365 Epoch 9/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.3063 - d_loss: 0.5735 Epoch 10/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.9733 - d_loss: 0.5438 Epoch 11/500 469/469 [==============================] - 31s 66ms/step - g_loss: 3.1071 - d_loss: 0.4692 Epoch 12/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.2592 - d_loss: 0.5639 Epoch 13/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.6841 - d_loss: 0.5902 Epoch 14/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.8386 - d_loss: 0.6068 Epoch 15/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.0518 - d_loss: 0.6455 Epoch 16/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.2930 - d_loss: 0.6596 Epoch 17/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.5129 - d_loss: 0.6231 Epoch 18/500 469/469 [==============================] - 31s 67ms/step - g_loss: 1.4004 - d_loss: 0.5881 Epoch 19/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.2480 - d_loss: 0.6412 Epoch 20/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.0937 - d_loss: 0.6156 Epoch 21/500 469/469 [==============================] - 31s 67ms/step - g_loss: 1.8643 - d_loss: 0.6647 Epoch 22/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.4884 - d_loss: 0.6188 Epoch 23/500 469/469 [==============================] - 32s 67ms/step - g_loss: 1.5896 - d_loss: 0.5323 Epoch 24/500 469/469 [==============================] - 32s 67ms/step - g_loss: 1.4624 - d_loss: 0.5852 Epoch 25/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.2889 - d_loss: 0.6373 Epoch 26/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.0778 - d_loss: 0.6703 Epoch 27/500 469/469 [==============================] - 32s 67ms/step - g_loss: 1.2800 - d_loss: 0.6577 Epoch 28/500 469/469 [==============================] - 30s 63ms/step - g_loss: 1.4507 - d_loss: 0.6001 Epoch 29/500 469/469 [==============================] - 30s 63ms/step - g_loss: 1.4553 - d_loss: 0.6230 Epoch 30/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2157 - d_loss: 0.6063 Epoch 31/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2933 - d_loss: 0.6112 Epoch 32/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.7231 - d_loss: 0.5880 Epoch 33/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.3563 - d_loss: 0.6291 Epoch 34/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1785 - d_loss: 0.6291 Epoch 35/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.4564 - d_loss: 0.6526 Epoch 36/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0682 - d_loss: 0.6396 Epoch 37/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2467 - d_loss: 0.6366 Epoch 38/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2528 - d_loss: 0.6424 Epoch 39/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2712 - d_loss: 0.6583 Epoch 40/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2363 - d_loss: 0.6464 Epoch 41/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.3939 - d_loss: 0.6094 Epoch 42/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2085 - d_loss: 0.6505 Epoch 43/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1332 - d_loss: 0.7253 Epoch 44/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2477 - d_loss: 0.6606 Epoch 45/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1430 - d_loss: 0.6931 Epoch 46/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0633 - d_loss: 0.6925 Epoch 47/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2437 - d_loss: 0.6531 Epoch 48/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9982 - d_loss: 0.6242 Epoch 49/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2499 - d_loss: 0.6750 Epoch 50/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0597 - d_loss: 0.6142 Epoch 51/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0214 - d_loss: 0.6563 Epoch 52/500 469/469 [==============================] - 29s 62ms/step - g_loss: 2.5056 - d_loss: 0.6448 Epoch 53/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1529 - d_loss: 0.6487 Epoch 54/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2820 - d_loss: 0.6681 Epoch 55/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0876 - d_loss: 0.6214 Epoch 56/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0108 - d_loss: 0.6619 Epoch 57/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2736 - d_loss: 0.6838 Epoch 58/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2120 - d_loss: 0.6904 Epoch 59/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.7041 - d_loss: 0.6691 Epoch 60/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0308 - d_loss: 0.6399 Epoch 61/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.4339 - d_loss: 0.6551 Epoch 62/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1791 - d_loss: 0.6584 Epoch 63/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2118 - d_loss: 0.6505 Epoch 64/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2520 - d_loss: 0.6206 Epoch 65/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2695 - d_loss: 0.6596 Epoch 66/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.4408 - d_loss: 0.6739 Epoch 67/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2648 - d_loss: 0.6669 Epoch 68/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2673 - d_loss: 0.6763 Epoch 69/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2625 - d_loss: 0.6861 Epoch 70/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0483 - d_loss: 0.6822 Epoch 71/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0664 - d_loss: 0.7015 Epoch 72/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0084 - d_loss: 0.6351 Epoch 73/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.3474 - d_loss: 0.7115 Epoch 74/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9825 - d_loss: 0.6724 Epoch 75/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1624 - d_loss: 0.6217 Epoch 76/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2071 - d_loss: 0.6694 Epoch 77/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1885 - d_loss: 0.6333 Epoch 78/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1189 - d_loss: 0.6193 Epoch 79/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.4141 - d_loss: 0.6432 Epoch 80/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1124 - d_loss: 0.6595 Epoch 81/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1269 - d_loss: 0.6781 Epoch 82/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2940 - d_loss: 0.6073 Epoch 83/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1051 - d_loss: 0.7107 Epoch 84/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2156 - d_loss: 0.6800 Epoch 85/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1016 - d_loss: 0.6025 Epoch 86/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2859 - d_loss: 0.6614 Epoch 87/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1656 - d_loss: 0.6522 Epoch 88/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0048 - d_loss: 0.6369 Epoch 89/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2100 - d_loss: 0.6506 Epoch 90/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.4153 - d_loss: 0.6282 Epoch 91/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.3949 - d_loss: 0.6293 Epoch 92/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2153 - d_loss: 0.6340 Epoch 93/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0294 - d_loss: 0.6380 Epoch 94/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0552 - d_loss: 0.6001 Epoch 95/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1166 - d_loss: 0.6287 Epoch 96/500 469/469 [==============================] - 29s 61ms/step - g_loss: 0.9448 - d_loss: 0.6611 Epoch 97/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0431 - d_loss: 0.7509 Epoch 98/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0750 - d_loss: 0.6858 Epoch 99/500 469/469 [==============================] - 29s 61ms/step - g_loss: 0.9663 - d_loss: 0.6698 Epoch 100/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.5134 - d_loss: 0.6156 Epoch 101/500 469/469 [==============================] - 29s 61ms/step - g_loss: 0.9494 - d_loss: 0.7379 Epoch 102/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1367 - d_loss: 0.6523 Epoch 103/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2171 - d_loss: 0.6305 Epoch 104/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1237 - d_loss: 0.6524 Epoch 105/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1529 - d_loss: 0.6631 Epoch 106/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2568 - d_loss: 0.6253 Epoch 107/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0935 - d_loss: 0.6947 Epoch 108/500 469/469 [==============================] - 29s 61ms/step - g_loss: 0.9868 - d_loss: 0.6491 Epoch 109/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2971 - d_loss: 0.6612 Epoch 110/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9946 - d_loss: 0.6839 Epoch 111/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1197 - d_loss: 0.7165 Epoch 112/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1527 - d_loss: 0.6888 Epoch 113/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0372 - d_loss: 0.6242 Epoch 114/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1247 - d_loss: 0.6829 Epoch 115/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1177 - d_loss: 0.6372 Epoch 116/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2225 - d_loss: 0.6778 Epoch 117/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0861 - d_loss: 0.6122 Epoch 118/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1619 - d_loss: 0.6536 Epoch 119/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0808 - d_loss: 0.6412 Epoch 120/500 469/469 [==============================] - 29s 61ms/step - g_loss: 0.9751 - d_loss: 0.6512 Epoch 121/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9658 - d_loss: 0.6581 Epoch 122/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1793 - d_loss: 0.6557 Epoch 123/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2395 - d_loss: 0.6659 Epoch 124/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0807 - d_loss: 0.6186 Epoch 125/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1746 - d_loss: 0.6183 Epoch 126/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2166 - d_loss: 0.6219 Epoch 127/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1416 - d_loss: 0.6029 Epoch 128/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.4293 - d_loss: 0.6989 Epoch 129/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.4114 - d_loss: 0.6587 Epoch 130/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0954 - d_loss: 0.6192 Epoch 131/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.6392 - d_loss: 0.5806 Epoch 132/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3736 - d_loss: 0.6016 Epoch 133/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3163 - d_loss: 0.5674 Epoch 134/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.4069 - d_loss: 0.6261 Epoch 135/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3511 - d_loss: 0.5846 Epoch 136/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3544 - d_loss: 0.5710 Epoch 137/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.4034 - d_loss: 0.6200 Epoch 138/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2965 - d_loss: 0.6011 Epoch 139/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1740 - d_loss: 0.6278 Epoch 140/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1208 - d_loss: 0.6275 Epoch 141/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0921 - d_loss: 0.6645 Epoch 142/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2142 - d_loss: 0.6096 Epoch 143/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1653 - d_loss: 0.5884 Epoch 144/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1613 - d_loss: 0.6278 Epoch 145/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2431 - d_loss: 0.5755 Epoch 146/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0998 - d_loss: 0.6300 Epoch 147/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1860 - d_loss: 0.6041 Epoch 148/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2014 - d_loss: 0.6316 Epoch 149/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2250 - d_loss: 0.5942 Epoch 150/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0708 - d_loss: 0.6155 Epoch 151/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1233 - d_loss: 0.6412 Epoch 152/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1461 - d_loss: 0.6399 Epoch 153/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2068 - d_loss: 0.6001 Epoch 154/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1975 - d_loss: 0.6179 Epoch 155/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0919 - d_loss: 0.6200 Epoch 156/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0775 - d_loss: 0.6038 Epoch 157/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1558 - d_loss: 0.6381 Epoch 158/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.2677 - d_loss: 0.6270 Epoch 159/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0241 - d_loss: 0.6214 Epoch 160/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1969 - d_loss: 0.6115 Epoch 161/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0703 - d_loss: 0.6267 Epoch 162/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1768 - d_loss: 0.6138 Epoch 163/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1277 - d_loss: 0.6171 Epoch 164/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9460 - d_loss: 0.6483 Epoch 165/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3630 - d_loss: 0.6449 Epoch 166/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0458 - d_loss: 0.6168 Epoch 167/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2076 - d_loss: 0.5920 Epoch 168/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3258 - d_loss: 0.6243 Epoch 169/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0003 - d_loss: 0.6210 Epoch 170/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0412 - d_loss: 0.6172 Epoch 171/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3498 - d_loss: 0.6162 Epoch 172/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0600 - d_loss: 0.6324 Epoch 173/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0487 - d_loss: 0.5938 Epoch 174/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3312 - d_loss: 0.6318 Epoch 175/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2548 - d_loss: 0.6591 Epoch 176/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2532 - d_loss: 0.6128 Epoch 177/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1944 - d_loss: 0.6682 Epoch 178/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9803 - d_loss: 0.6127 Epoch 179/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2363 - d_loss: 0.6033 Epoch 180/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1815 - d_loss: 0.6252 Epoch 181/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2046 - d_loss: 0.6297 Epoch 182/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1660 - d_loss: 0.5873 Epoch 183/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2528 - d_loss: 0.6434 Epoch 184/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1003 - d_loss: 0.6198 Epoch 185/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2695 - d_loss: 0.5980 Epoch 186/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1954 - d_loss: 0.5599 Epoch 187/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1703 - d_loss: 0.6024 Epoch 188/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0782 - d_loss: 0.6068 Epoch 189/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3979 - d_loss: 0.6731 Epoch 190/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0145 - d_loss: 0.6039 Epoch 191/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1637 - d_loss: 0.5895 Epoch 192/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1626 - d_loss: 0.6062 Epoch 193/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2904 - d_loss: 0.6347 Epoch 194/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0810 - d_loss: 0.5926 Epoch 195/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1230 - d_loss: 0.5827 Epoch 196/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.3168 - d_loss: 0.5943 Epoch 197/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2563 - d_loss: 0.6468 Epoch 198/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0763 - d_loss: 0.6046 Epoch 199/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2684 - d_loss: 0.6089 Epoch 200/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1765 - d_loss: 0.5994 Epoch 201/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.0043 - d_loss: 0.6340 Epoch 202/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.2710 - d_loss: 0.6035 Epoch 203/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1598 - d_loss: 0.6063 Epoch 204/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1042 - d_loss: 0.6035 Epoch 205/500 469/469 [==============================] - 29s 61ms/step - g_loss: 1.1020 - d_loss: 0.6249 Epoch 206/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9914 - d_loss: 0.6196 Epoch 207/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0914 - d_loss: 0.6136 Epoch 208/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0732 - d_loss: 0.6093 Epoch 209/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1021 - d_loss: 0.5883 Epoch 210/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1276 - d_loss: 0.6476 Epoch 211/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9357 - d_loss: 0.6425 Epoch 212/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0733 - d_loss: 0.6451 Epoch 213/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9743 - d_loss: 0.6936 Epoch 214/500 469/469 [==============================] - 29s 62ms/step - g_loss: 0.9775 - d_loss: 0.6763 Epoch 215/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.0743 - d_loss: 0.6550 Epoch 216/500 469/469 [==============================] - 29s 62ms/step - g_loss: 1.1067 - d_loss: 0.6293 Epoch 217/500 469/469 [==============================] - 30s 65ms/step - g_loss: 1.1021 - d_loss: 0.6183 Epoch 218/500 469/469 [==============================] - 31s 65ms/step - g_loss: 1.2435 - d_loss: 0.6153 Epoch 219/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0467 - d_loss: 0.6542 Epoch 220/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0166 - d_loss: 0.6395 Epoch 221/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.1732 - d_loss: 0.6587 Epoch 222/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0507 - d_loss: 0.6345 Epoch 223/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0224 - d_loss: 0.6199 Epoch 224/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.1198 - d_loss: 0.6275 Epoch 225/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0196 - d_loss: 0.6522 Epoch 226/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0951 - d_loss: 0.5968 Epoch 227/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.1104 - d_loss: 0.6262 Epoch 228/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0295 - d_loss: 0.6139 Epoch 229/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.1992 - d_loss: 0.6534 Epoch 230/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0255 - d_loss: 0.6212 Epoch 231/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0062 - d_loss: 0.6153 Epoch 232/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.1987 - d_loss: 0.6509 Epoch 233/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.0113 - d_loss: 0.6477 Epoch 234/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.3565 - d_loss: 0.6513 Epoch 235/500 469/469 [==============================] - 30s 64ms/step - g_loss: 0.9757 - d_loss: 0.6244 Epoch 236/500 469/469 [==============================] - 30s 65ms/step - g_loss: 1.0178 - d_loss: 0.6244 Epoch 237/500 469/469 [==============================] - 31s 67ms/step - g_loss: 1.0986 - d_loss: 0.6093 Epoch 238/500 469/469 [==============================] - 30s 63ms/step - g_loss: 1.0219 - d_loss: 0.6363 Epoch 239/500 469/469 [==============================] - 30s 64ms/step - g_loss: 1.2610 - d_loss: 0.6355 Epoch 240/500 469/469 [==============================] - 31s 66ms/step - g_loss: 1.1867 - d_loss: 0.6412 Epoch 241/500 469/469 [==============================] - 31s 65ms/step - g_loss: 1.1613 - d_loss: 0.5954 Epoch 242/500 469/469 [==============================] - 31s 67ms/step - g_loss: 1.1450 - d_loss: 0.6045 Epoch 243/500 469/469 [==============================] - 31s 67ms/step - g_loss: 0.9961 - d_loss: 0.6302 Epoch 244/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.1190 - d_loss: 0.6053 Epoch 245/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.0823 - d_loss: 0.6227 Epoch 246/500 469/469 [==============================] - 32s 69ms/step - g_loss: 0.9860 - d_loss: 0.6178 Epoch 247/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.3416 - d_loss: 0.6834 Epoch 248/500 469/469 [==============================] - 32s 69ms/step - g_loss: 0.9616 - d_loss: 0.6244 Epoch 249/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.2432 - d_loss: 0.5942 Epoch 250/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.2114 - d_loss: 0.6198 Epoch 251/500 469/469 [==============================] - 32s 67ms/step - g_loss: 1.1337 - d_loss: 0.5973 Epoch 252/500 469/469 [==============================] - 31s 67ms/step - g_loss: 1.0505 - d_loss: 0.6183 Epoch 253/500 469/469 [==============================] - 32s 67ms/step - g_loss: 1.1043 - d_loss: 0.6104 Epoch 254/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.1428 - d_loss: 0.6218 Epoch 255/500 469/469 [==============================] - 31s 67ms/step - g_loss: 1.0686 - d_loss: 0.6242 Epoch 256/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.0603 - d_loss: 0.6065 Epoch 257/500 469/469 [==============================] - 32s 67ms/step - g_loss: 1.1076 - d_loss: 0.6240 Epoch 258/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.0720 - d_loss: 0.6006 Epoch 259/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.0726 - d_loss: 0.6260 Epoch 260/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.0388 - d_loss: 0.6582 Epoch 261/500 469/469 [==============================] - 33s 70ms/step - g_loss: 1.0563 - d_loss: 0.6182 Epoch 262/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.1997 - d_loss: 0.6703 Epoch 263/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.0343 - d_loss: 0.6006 Epoch 264/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.1457 - d_loss: 0.6135 Epoch 265/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.0964 - d_loss: 0.6181 Epoch 266/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.5886 - d_loss: 0.6213 Epoch 267/500 469/469 [==============================] - 33s 70ms/step - g_loss: 1.0110 - d_loss: 0.6149 Epoch 268/500 469/469 [==============================] - 33s 70ms/step - g_loss: 1.0735 - d_loss: 0.6025 Epoch 269/500 469/469 [==============================] - 34s 72ms/step - g_loss: 1.1784 - d_loss: 0.6053 Epoch 270/500 469/469 [==============================] - 33s 71ms/step - g_loss: 1.0148 - d_loss: 0.6062 Epoch 271/500 469/469 [==============================] - 33s 70ms/step - g_loss: 1.0506 - d_loss: 0.6036 Epoch 272/500 469/469 [==============================] - 33s 71ms/step - g_loss: 1.0669 - d_loss: 0.5993 Epoch 273/500 469/469 [==============================] - 33s 70ms/step - g_loss: 1.1067 - d_loss: 0.6080 Epoch 274/500 469/469 [==============================] - 33s 71ms/step - g_loss: 0.9959 - d_loss: 0.6291 Epoch 275/500 469/469 [==============================] - 33s 70ms/step - g_loss: 1.0815 - d_loss: 0.6019 Epoch 276/500 469/469 [==============================] - 33s 70ms/step - g_loss: 1.0007 - d_loss: 0.6196 Epoch 277/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.0505 - d_loss: 0.6195 Epoch 278/500 469/469 [==============================] - 33s 71ms/step - g_loss: 1.0393 - d_loss: 0.6272 Epoch 279/500 469/469 [==============================] - 33s 71ms/step - g_loss: 1.1016 - d_loss: 0.6218 Epoch 280/500 469/469 [==============================] - 33s 71ms/step - g_loss: 1.0374 - d_loss: 0.6033 Epoch 281/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.1515 - d_loss: 0.6227 Epoch 282/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.0232 - d_loss: 0.6610 Epoch 283/500 469/469 [==============================] - 33s 70ms/step - g_loss: 1.0071 - d_loss: 0.5931 Epoch 284/500 469/469 [==============================] - 33s 70ms/step - g_loss: 1.0213 - d_loss: 0.6109 Epoch 285/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.1038 - d_loss: 0.6253 Epoch 286/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.0006 - d_loss: 0.6009 Epoch 287/500 469/469 [==============================] - 32s 68ms/step - g_loss: 0.9800 - d_loss: 0.6192 Epoch 288/500 469/469 [==============================] - 32s 69ms/step - g_loss: 1.1458 - d_loss: 0.6035 Epoch 289/500 469/469 [==============================] - 32s 69ms/step - g_loss: 0.9618 - d_loss: 0.6311 Epoch 290/500 469/469 [==============================] - 32s 68ms/step - g_loss: 1.0915 - d_loss: 0.6014 Epoch 291/500 469/469 [==============================] - 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28s 59ms/step - g_loss: 4.5295 - d_loss: 0.1900 Epoch 402/500 469/469 [==============================] - 28s 59ms/step - g_loss: 4.6067 - d_loss: 0.1913 Epoch 403/500 469/469 [==============================] - 28s 60ms/step - g_loss: 4.6351 - d_loss: 0.1830 Epoch 404/500 469/469 [==============================] - 28s 59ms/step - g_loss: 4.7075 - d_loss: 0.1871 Epoch 405/500 469/469 [==============================] - 28s 59ms/step - g_loss: 4.7046 - d_loss: 0.1788 Epoch 406/500 469/469 [==============================] - 28s 59ms/step - g_loss: 4.7863 - d_loss: 0.1766 Epoch 407/500 469/469 [==============================] - 28s 59ms/step - g_loss: 4.8280 - d_loss: 0.1794 Epoch 408/500 469/469 [==============================] - 28s 59ms/step - g_loss: 4.8693 - d_loss: 0.1740 Epoch 409/500 469/469 [==============================] - 28s 59ms/step - g_loss: 4.8904 - d_loss: 0.1711 Epoch 410/500 469/469 [==============================] - 28s 59ms/step - g_loss: 4.9235 - d_loss: 0.1682 Epoch 411/500 469/469 [==============================] - 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28s 59ms/step - g_loss: 6.7932 - d_loss: 0.1236 Epoch 472/500 469/469 [==============================] - 28s 59ms/step - g_loss: 6.7623 - d_loss: 0.1226 Epoch 473/500 469/469 [==============================] - 28s 59ms/step - g_loss: 6.7413 - d_loss: 0.1210 Epoch 474/500 469/469 [==============================] - 28s 59ms/step - g_loss: 6.7777 - d_loss: 0.1115 Epoch 475/500 469/469 [==============================] - 28s 59ms/step - g_loss: 6.8507 - d_loss: 0.1123 Epoch 476/500 469/469 [==============================] - 28s 59ms/step - g_loss: 6.9196 - d_loss: 0.1185 Epoch 477/500 469/469 [==============================] - 28s 60ms/step - g_loss: 6.9097 - d_loss: 0.1101 Epoch 478/500 469/469 [==============================] - 28s 60ms/step - g_loss: 6.9067 - d_loss: 0.1104 Epoch 479/500 469/469 [==============================] - 28s 60ms/step - g_loss: 6.9420 - d_loss: 0.1231 Epoch 480/500 469/469 [==============================] - 28s 60ms/step - g_loss: 6.9866 - d_loss: 0.1190 Epoch 481/500 469/469 [==============================] - 28s 60ms/step - g_loss: 6.9584 - d_loss: 0.1186 Epoch 482/500 469/469 [==============================] - 28s 60ms/step - g_loss: 7.0272 - d_loss: 0.1152 Epoch 483/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.0189 - d_loss: 0.1084 Epoch 484/500 469/469 [==============================] - 28s 59ms/step - g_loss: 6.9383 - d_loss: 0.1158 Epoch 485/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.0687 - d_loss: 0.1094 Epoch 486/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.0942 - d_loss: 0.1141 Epoch 487/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.1188 - d_loss: 0.1124 Epoch 488/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.1198 - d_loss: 0.1036 Epoch 489/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.1202 - d_loss: 0.1160 Epoch 490/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.0597 - d_loss: 0.1135 Epoch 491/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.1890 - d_loss: 0.1036 Epoch 492/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.2254 - d_loss: 0.1014 Epoch 493/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.2345 - d_loss: 0.1065 Epoch 494/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.3192 - d_loss: 0.1056 Epoch 495/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.1879 - d_loss: 0.1095 Epoch 496/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.2857 - d_loss: 0.1032 Epoch 497/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.2520 - d_loss: 0.1044 Epoch 498/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.4426 - d_loss: 0.0992 Epoch 499/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.3361 - d_loss: 0.1006 Epoch 500/500 469/469 [==============================] - 28s 59ms/step - g_loss: 7.4492 - d_loss: 0.0953
# story history object into dataframe
hist_df = pd.DataFrame(con_hist.history)
# using pandas dataframe to plot out learning curve
with plt.style.context('seaborn'):
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8), tight_layout=True)
hist_df.loc[:, ["d_loss", 'g_loss']].plot(ax=ax1, title=r'Learning Curve of Loss Function CE')
plt.show()
tf.keras.models.save_model(generator , "./generator_cgan3.h5")
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
tf.keras.models.save_model(discriminator , "./discriminator_cgan3.h5")
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
# load model and plot generated images with labels
generator = tf.keras.models.load_model("./generator_cgan3.h5")
discriminator = tf.keras.models.load_model("./discriminator_cgan3.h5")
labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
random_noise = tf.random.normal(shape=(10, 128))
class_label = keras.utils.to_categorical([0], 10)
class_label = tf.cast(class_label, tf.float32)
class_label = tf.repeat(class_label, repeats=10, axis=0)
noise_and_label = tf.concat([random_noise, class_label], axis=1)
fake_images = generator(noise_and_label)
fake_images = tf.image.convert_image_dtype(fake_images, dtype=tf.float32, saturate=True)
fig, axs = plt.subplots(1, 10, figsize=(10, 1))
for j in range(10):
axs[j].imshow(fake_images[j])
axs[j].set_title(labels[j])
axs[j].axis("off")
plt.show()
cond_gan2 = ConditionalGAN(
discriminator=discriminator, generator=generator, latent_dim=128
)
cond_gan2.compile(
d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
loss_fn=keras.losses.BinaryCrossentropy(from_logits=False),
)
con_hist2 = cond_gan2.fit(dataset, epochs=1000, callbacks=[CONDGANMonitor(num_img=100, latent_dim=138)])
Epoch 1/1000 469/469 [==============================] - 29s 59ms/step - g_loss: 7.2806 - d_loss: 0.0968 Epoch 2/1000 468/469 [============================>.] - ETA: 0s - g_loss: 7.3500 - d_loss: 0.0911
469/469 [==============================] - 29s 63ms/step - g_loss: 7.3499 - d_loss: 0.0911 Epoch 3/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.3813 - d_loss: 0.0830 Epoch 4/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.4428 - d_loss: 0.0788 Epoch 5/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.5195 - d_loss: 0.0794 Epoch 6/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.6054 - d_loss: 0.0762 Epoch 7/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.6070 - d_loss: 0.0779 Epoch 8/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.6992 - d_loss: 0.0698 Epoch 9/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.7475 - d_loss: 0.0698 Epoch 10/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.8132 - d_loss: 0.0700 Epoch 11/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.9379 - d_loss: 0.0686 Epoch 12/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.0012 - d_loss: 0.0702 Epoch 13/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.0286 - d_loss: 0.0656 Epoch 14/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.0387 - d_loss: 0.0664 Epoch 15/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.0484 - d_loss: 0.0642 Epoch 16/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.1628 - d_loss: 0.0679 Epoch 17/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.1262 - d_loss: 0.0614 Epoch 18/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.2860 - d_loss: 0.0604 Epoch 19/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.3172 - d_loss: 0.0649 Epoch 20/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.4073 - d_loss: 0.0603 Epoch 21/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.5065 - d_loss: 0.0617 Epoch 22/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.5178 - d_loss: 0.0613 Epoch 23/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.5207 - d_loss: 0.0580 Epoch 24/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.5567 - d_loss: 0.0548 Epoch 25/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.5618 - d_loss: 0.0569 Epoch 26/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.6986 - d_loss: 0.0573 Epoch 27/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.7461 - d_loss: 0.0543 Epoch 28/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.7200 - d_loss: 0.0522 Epoch 29/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.8150 - d_loss: 0.0532 Epoch 30/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.9656 - d_loss: 0.0507 Epoch 31/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.8789 - d_loss: 0.0517 Epoch 32/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.9274 - d_loss: 0.0554 Epoch 33/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.0310 - d_loss: 0.0527 Epoch 34/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.9713 - d_loss: 0.0501 Epoch 35/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.0907 - d_loss: 0.0501 Epoch 36/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.1317 - d_loss: 0.0499 Epoch 37/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.2080 - d_loss: 0.0470 Epoch 38/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.2349 - d_loss: 0.0458 Epoch 39/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.2621 - d_loss: 0.0414 Epoch 40/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.3861 - d_loss: 0.0453 Epoch 41/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.3884 - d_loss: 0.0446 Epoch 42/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.4506 - d_loss: 0.0426 Epoch 43/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.5428 - d_loss: 0.0412 Epoch 44/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.5522 - d_loss: 0.0404 Epoch 45/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.5992 - d_loss: 0.0338 Epoch 46/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.6825 - d_loss: 0.0382 Epoch 47/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.8346 - d_loss: 0.0355 Epoch 48/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.8962 - d_loss: 0.0357 Epoch 49/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.0573 - d_loss: 0.0363 Epoch 50/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.1319 - d_loss: 0.0374 Epoch 51/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.3873 - d_loss: 0.0392 Epoch 52/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.6272 - d_loss: 0.0544 Epoch 53/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.2014 - d_loss: 0.0543 Epoch 54/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.2400 - d_loss: 0.0590 Epoch 55/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.2904 - d_loss: 0.0705 Epoch 56/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.4045 - d_loss: 0.0806 Epoch 57/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.1778 - d_loss: 0.0803 Epoch 58/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 10.3531 - d_loss: 0.0856 Epoch 59/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.6527 - d_loss: 0.1053 Epoch 60/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.4508 - d_loss: 0.1092 Epoch 61/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.3527 - d_loss: 0.1054 Epoch 62/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 9.1470 - d_loss: 0.1094 Epoch 63/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.8261 - d_loss: 0.1228 Epoch 64/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.7799 - d_loss: 0.1320 Epoch 65/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.6814 - d_loss: 0.1182 Epoch 66/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.5746 - d_loss: 0.1234 Epoch 67/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.2348 - d_loss: 0.1541 Epoch 68/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.2073 - d_loss: 0.1417 Epoch 69/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.3819 - d_loss: 0.1401 Epoch 70/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.5086 - d_loss: 0.1361 Epoch 71/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 8.2303 - d_loss: 0.1524 Epoch 72/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.7880 - d_loss: 0.1596 Epoch 73/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.6305 - d_loss: 0.1396 Epoch 74/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.6288 - d_loss: 0.1492 Epoch 75/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.7561 - d_loss: 0.1525 Epoch 76/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.5506 - d_loss: 0.1577 Epoch 77/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.6038 - d_loss: 0.1612 Epoch 78/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.6432 - d_loss: 0.1424 Epoch 79/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.4179 - d_loss: 0.1555 Epoch 80/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.1561 - d_loss: 0.1639 Epoch 81/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.2795 - d_loss: 0.1654 Epoch 82/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.0261 - d_loss: 0.1658 Epoch 83/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.4132 - d_loss: 0.1545 Epoch 84/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.2574 - d_loss: 0.1594 Epoch 85/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 7.1168 - d_loss: 0.1664 Epoch 86/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.8404 - d_loss: 0.1900 Epoch 87/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.5135 - d_loss: 0.1831 Epoch 88/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3927 - d_loss: 0.1866 Epoch 89/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.6183 - d_loss: 0.1778 Epoch 90/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.5279 - d_loss: 0.1551 Epoch 91/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3045 - d_loss: 0.1824 Epoch 92/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2790 - d_loss: 0.1782 Epoch 93/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4606 - d_loss: 0.1618 Epoch 94/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4857 - d_loss: 0.1695 Epoch 95/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3293 - d_loss: 0.1651 Epoch 96/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3219 - d_loss: 0.1692 Epoch 97/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2601 - d_loss: 0.1728 Epoch 98/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0038 - d_loss: 0.1827 Epoch 99/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9246 - d_loss: 0.2001 Epoch 100/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8320 - d_loss: 0.1967 Epoch 101/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7747 - d_loss: 0.1884 Epoch 102/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9186 - d_loss: 0.1706 Epoch 103/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7886 - d_loss: 0.2000 Epoch 104/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8162 - d_loss: 0.1839 Epoch 105/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8899 - d_loss: 0.1696 Epoch 106/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9539 - d_loss: 0.1631 Epoch 107/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8343 - d_loss: 0.1797 Epoch 108/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0332 - d_loss: 0.1576 Epoch 109/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7992 - d_loss: 0.1664 Epoch 110/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6953 - d_loss: 0.1721 Epoch 111/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9190 - d_loss: 0.1584 Epoch 112/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6948 - d_loss: 0.1759 Epoch 113/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6091 - d_loss: 0.1863 Epoch 114/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6886 - d_loss: 0.1734 Epoch 115/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6252 - d_loss: 0.1837 Epoch 116/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5606 - d_loss: 0.1804 Epoch 117/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4291 - d_loss: 0.1902 Epoch 118/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5511 - d_loss: 0.1675 Epoch 119/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6379 - d_loss: 0.1669 Epoch 120/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5597 - d_loss: 0.1847 Epoch 121/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3821 - d_loss: 0.1893 Epoch 122/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3460 - d_loss: 0.1898 Epoch 123/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3890 - d_loss: 0.1799 Epoch 124/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6055 - d_loss: 0.1687 Epoch 125/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3721 - d_loss: 0.1840 Epoch 126/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4574 - d_loss: 0.1601 Epoch 127/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7532 - d_loss: 0.1612 Epoch 128/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4744 - d_loss: 0.1731 Epoch 129/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4976 - d_loss: 0.1867 Epoch 130/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2705 - d_loss: 0.2066 Epoch 131/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3130 - d_loss: 0.1726 Epoch 132/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3736 - d_loss: 0.1854 Epoch 133/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2893 - d_loss: 0.1790 Epoch 134/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1098 - d_loss: 0.1932 Epoch 135/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3785 - d_loss: 0.1765 Epoch 136/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2075 - d_loss: 0.1853 Epoch 137/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3235 - d_loss: 0.1782 Epoch 138/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3254 - d_loss: 0.1762 Epoch 139/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0640 - d_loss: 0.1910 Epoch 140/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3063 - d_loss: 0.1834 Epoch 141/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0702 - d_loss: 0.2079 Epoch 142/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2101 - d_loss: 0.1652 Epoch 143/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2404 - d_loss: 0.1682 Epoch 144/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3324 - d_loss: 0.1621 Epoch 145/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2818 - d_loss: 0.1734 Epoch 146/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3773 - d_loss: 0.1611 Epoch 147/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3066 - d_loss: 0.1740 Epoch 148/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2212 - d_loss: 0.1967 Epoch 149/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1301 - d_loss: 0.1942 Epoch 150/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1687 - d_loss: 0.1872 Epoch 151/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 4.9384 - d_loss: 0.2034 Epoch 152/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1495 - d_loss: 0.1765 Epoch 153/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2170 - d_loss: 0.1647 Epoch 154/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1318 - d_loss: 0.1972 Epoch 155/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3150 - d_loss: 0.1663 Epoch 156/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3817 - d_loss: 0.1520 Epoch 157/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4246 - d_loss: 0.1548 Epoch 158/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3199 - d_loss: 0.1644 Epoch 159/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3431 - d_loss: 0.1745 Epoch 160/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1796 - d_loss: 0.1871 Epoch 161/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0700 - d_loss: 0.1939 Epoch 162/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0672 - d_loss: 0.1793 Epoch 163/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0097 - d_loss: 0.1897 Epoch 164/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2046 - d_loss: 0.1659 Epoch 165/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4261 - d_loss: 0.1543 Epoch 166/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2574 - d_loss: 0.1720 Epoch 167/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0421 - d_loss: 0.1801 Epoch 168/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0472 - d_loss: 0.1850 Epoch 169/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1217 - d_loss: 0.1850 Epoch 170/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0698 - d_loss: 0.1715 Epoch 171/1000 469/469 [==============================] - 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28s 59ms/step - g_loss: 5.1007 - d_loss: 0.1721 Epoch 182/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 4.9691 - d_loss: 0.1840 Epoch 183/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 4.9194 - d_loss: 0.1904 Epoch 184/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1933 - d_loss: 0.1774 Epoch 185/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2800 - d_loss: 0.1728 Epoch 186/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3465 - d_loss: 0.1601 Epoch 187/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3369 - d_loss: 0.1727 Epoch 188/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0949 - d_loss: 0.1769 Epoch 189/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0523 - d_loss: 0.1849 Epoch 190/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0302 - d_loss: 0.1920 Epoch 191/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 4.9352 - d_loss: 0.1981 Epoch 192/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0741 - d_loss: 0.1757 Epoch 193/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3234 - d_loss: 0.1502 Epoch 194/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2673 - d_loss: 0.1593 Epoch 195/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0119 - d_loss: 0.1810 Epoch 196/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1694 - d_loss: 0.1878 Epoch 197/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3567 - d_loss: 0.1586 Epoch 198/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2332 - d_loss: 0.1689 Epoch 199/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2637 - d_loss: 0.1680 Epoch 200/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1214 - d_loss: 0.1753 Epoch 201/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2208 - d_loss: 0.1683 Epoch 202/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3505 - d_loss: 0.1529 Epoch 203/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3731 - d_loss: 0.1580 Epoch 204/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1799 - d_loss: 0.1814 Epoch 205/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 4.9585 - d_loss: 0.1919 Epoch 206/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1826 - d_loss: 0.1698 Epoch 207/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.0228 - d_loss: 0.1944 Epoch 208/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3241 - d_loss: 0.1568 Epoch 209/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2711 - d_loss: 0.1635 Epoch 210/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4192 - d_loss: 0.1643 Epoch 211/1000 469/469 [==============================] - 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28s 59ms/step - g_loss: 5.2055 - d_loss: 0.1763 Epoch 272/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3146 - d_loss: 0.1686 Epoch 273/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5990 - d_loss: 0.1524 Epoch 274/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5414 - d_loss: 0.1567 Epoch 275/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6116 - d_loss: 0.1489 Epoch 276/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6079 - d_loss: 0.1474 Epoch 277/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2241 - d_loss: 0.1891 Epoch 278/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2399 - d_loss: 0.1667 Epoch 279/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4015 - d_loss: 0.1525 Epoch 280/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4142 - d_loss: 0.1567 Epoch 281/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3608 - d_loss: 0.1779 Epoch 282/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1444 - d_loss: 0.1848 Epoch 283/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.1544 - d_loss: 0.1856 Epoch 284/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2351 - d_loss: 0.1615 Epoch 285/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6739 - d_loss: 0.1399 Epoch 286/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8143 - d_loss: 0.1394 Epoch 287/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6571 - d_loss: 0.1452 Epoch 288/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5223 - d_loss: 0.1542 Epoch 289/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2168 - d_loss: 0.1891 Epoch 290/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3881 - d_loss: 0.1583 Epoch 291/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4257 - d_loss: 0.1598 Epoch 292/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3676 - d_loss: 0.1604 Epoch 293/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6807 - d_loss: 0.1421 Epoch 294/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3905 - d_loss: 0.1701 Epoch 295/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5129 - d_loss: 0.1608 Epoch 296/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5283 - d_loss: 0.1508 Epoch 297/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5717 - d_loss: 0.1699 Epoch 298/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4784 - d_loss: 0.1658 Epoch 299/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4520 - d_loss: 0.1630 Epoch 300/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.2932 - d_loss: 0.1888 Epoch 301/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5533 - d_loss: 0.1395 Epoch 302/1000 468/469 [============================>.] - ETA: 0s - g_loss: 5.4903 - d_loss: 0.1552
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g_loss: 5.4404 - d_loss: 0.1525 Epoch 312/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3446 - d_loss: 0.1767 Epoch 313/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5157 - d_loss: 0.1498 Epoch 314/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6843 - d_loss: 0.1518 Epoch 315/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4806 - d_loss: 0.1695 Epoch 316/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6044 - d_loss: 0.1497 Epoch 317/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4524 - d_loss: 0.1551 Epoch 318/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.4490 - d_loss: 0.1619 Epoch 319/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5072 - d_loss: 0.1650 Epoch 320/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9622 - d_loss: 0.1412 Epoch 321/1000 469/469 [==============================] - 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28s 59ms/step - g_loss: 5.3326 - d_loss: 0.1924 Epoch 372/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7861 - d_loss: 0.1379 Epoch 373/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9430 - d_loss: 0.1371 Epoch 374/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5706 - d_loss: 0.1494 Epoch 375/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8934 - d_loss: 0.1379 Epoch 376/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1028 - d_loss: 0.1312 Epoch 377/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0914 - d_loss: 0.1213 Epoch 378/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8783 - d_loss: 0.1544 Epoch 379/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6741 - d_loss: 0.1586 Epoch 380/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5164 - d_loss: 0.1663 Epoch 381/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6110 - d_loss: 0.1596 Epoch 382/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7055 - d_loss: 0.1575 Epoch 383/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6429 - d_loss: 0.1658 Epoch 384/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7134 - d_loss: 0.1500 Epoch 385/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7894 - d_loss: 0.1485 Epoch 386/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5804 - d_loss: 0.1603 Epoch 387/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6647 - d_loss: 0.1549 Epoch 388/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7642 - d_loss: 0.1597 Epoch 389/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6698 - d_loss: 0.1483 Epoch 390/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6117 - d_loss: 0.1581 Epoch 391/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7234 - d_loss: 0.1578 Epoch 392/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6320 - d_loss: 0.1565 Epoch 393/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9707 - d_loss: 0.1389 Epoch 394/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0242 - d_loss: 0.1182 Epoch 395/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2666 - d_loss: 0.1178 Epoch 396/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4013 - d_loss: 0.1079 Epoch 397/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1787 - d_loss: 0.1269 Epoch 398/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1914 - d_loss: 0.1282 Epoch 399/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4269 - d_loss: 0.1156 Epoch 400/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4857 - d_loss: 0.1249 Epoch 401/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9418 - d_loss: 0.1627 Epoch 402/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6911 - d_loss: 0.1681 Epoch 403/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8185 - d_loss: 0.1512 Epoch 404/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6014 - d_loss: 0.1660 Epoch 405/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0930 - d_loss: 0.1355 Epoch 406/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0765 - d_loss: 0.1380 Epoch 407/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9596 - d_loss: 0.1523 Epoch 408/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.3659 - d_loss: 0.1901 Epoch 409/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7940 - d_loss: 0.1471 Epoch 410/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8701 - d_loss: 0.1541 Epoch 411/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8839 - d_loss: 0.1477 Epoch 412/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8399 - d_loss: 0.1571 Epoch 413/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6682 - d_loss: 0.1601 Epoch 414/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6832 - d_loss: 0.1622 Epoch 415/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9584 - d_loss: 0.1516 Epoch 416/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9357 - d_loss: 0.1463 Epoch 417/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6161 - d_loss: 0.1716 Epoch 418/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.5137 - d_loss: 0.1606 Epoch 419/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8246 - d_loss: 0.1529 Epoch 420/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8763 - d_loss: 0.1417 Epoch 421/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9122 - d_loss: 0.1500 Epoch 422/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8632 - d_loss: 0.1563 Epoch 423/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0368 - d_loss: 0.1405 Epoch 424/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7387 - d_loss: 0.1637 Epoch 425/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0506 - d_loss: 0.1367 Epoch 426/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0265 - d_loss: 0.1420 Epoch 427/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9809 - d_loss: 0.1456 Epoch 428/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6162 - d_loss: 0.1628 Epoch 429/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8619 - d_loss: 0.1540 Epoch 430/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9556 - d_loss: 0.1467 Epoch 431/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0467 - d_loss: 0.1385 Epoch 432/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9876 - d_loss: 0.1243 Epoch 433/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0679 - d_loss: 0.1305 Epoch 434/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1303 - d_loss: 0.1261 Epoch 435/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9094 - d_loss: 0.1446 Epoch 436/1000 469/469 [==============================] - 27s 59ms/step - g_loss: 6.0689 - d_loss: 0.1378 Epoch 437/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9458 - d_loss: 0.1429 Epoch 438/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8771 - d_loss: 0.1569 Epoch 439/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7197 - d_loss: 0.1561 Epoch 440/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8801 - d_loss: 0.1467 Epoch 441/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0946 - d_loss: 0.1338 Epoch 442/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3828 - d_loss: 0.1332 Epoch 443/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9893 - d_loss: 0.1574 Epoch 444/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8510 - d_loss: 0.1649 Epoch 445/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6329 - d_loss: 0.1593 Epoch 446/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0699 - d_loss: 0.1337 Epoch 447/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0437 - d_loss: 0.1398 Epoch 448/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9309 - d_loss: 0.1481 Epoch 449/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.6924 - d_loss: 0.1678 Epoch 450/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8790 - d_loss: 0.1592 Epoch 451/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2698 - d_loss: 0.1195 Epoch 452/1000 469/469 [==============================] - 27s 59ms/step - g_loss: 6.7070 - d_loss: 0.1059 Epoch 453/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.7672 - d_loss: 0.0959 Epoch 454/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.9053 - d_loss: 0.0822 Epoch 455/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.7976 - d_loss: 0.0925 Epoch 456/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.7888 - d_loss: 0.1009 Epoch 457/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.6012 - d_loss: 0.1087 Epoch 458/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.5167 - d_loss: 0.1375 Epoch 459/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8543 - d_loss: 0.1714 Epoch 460/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0714 - d_loss: 0.1530 Epoch 461/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9205 - d_loss: 0.1725 Epoch 462/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0980 - d_loss: 0.1489 Epoch 463/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9485 - d_loss: 0.1727 Epoch 464/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1214 - d_loss: 0.1403 Epoch 465/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0182 - d_loss: 0.1483 Epoch 466/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9071 - d_loss: 0.1472 Epoch 467/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9428 - d_loss: 0.1603 Epoch 468/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8242 - d_loss: 0.1539 Epoch 469/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1469 - d_loss: 0.1265 Epoch 470/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0857 - d_loss: 0.1437 Epoch 471/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8794 - d_loss: 0.1502 Epoch 472/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9564 - d_loss: 0.1507 Epoch 473/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3458 - d_loss: 0.1197 Epoch 474/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2000 - d_loss: 0.1212 Epoch 475/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0539 - d_loss: 0.1364 Epoch 476/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0956 - d_loss: 0.1296 Epoch 477/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1149 - d_loss: 0.1417 Epoch 478/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0116 - d_loss: 0.1563 Epoch 479/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8771 - d_loss: 0.1570 Epoch 480/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1370 - d_loss: 0.1281 Epoch 481/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3957 - d_loss: 0.1175 Epoch 482/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4816 - d_loss: 0.1187 Epoch 483/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.6271 - d_loss: 0.1183 Epoch 484/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1759 - d_loss: 0.1397 Epoch 485/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9612 - d_loss: 0.1608 Epoch 486/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8575 - d_loss: 0.1564 Epoch 487/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9201 - d_loss: 0.1597 Epoch 488/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9429 - d_loss: 0.1461 Epoch 489/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1401 - d_loss: 0.1377 Epoch 490/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1609 - d_loss: 0.1439 Epoch 491/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0978 - d_loss: 0.1416 Epoch 492/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7436 - d_loss: 0.1618 Epoch 493/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8966 - d_loss: 0.1491 Epoch 494/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0866 - d_loss: 0.1421 Epoch 495/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9493 - d_loss: 0.1468 Epoch 496/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8449 - d_loss: 0.1526 Epoch 497/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0352 - d_loss: 0.1300 Epoch 498/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3793 - d_loss: 0.1178 Epoch 499/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3631 - d_loss: 0.1138 Epoch 500/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4376 - d_loss: 0.1018 Epoch 501/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4535 - d_loss: 0.1179 Epoch 502/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1617 - d_loss: 0.1416 Epoch 503/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0191 - d_loss: 0.1455 Epoch 504/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0227 - d_loss: 0.1507 Epoch 505/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9963 - d_loss: 0.1407 Epoch 506/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2580 - d_loss: 0.1412 Epoch 507/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2821 - d_loss: 0.1380 Epoch 508/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0147 - d_loss: 0.1669 Epoch 509/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9978 - d_loss: 0.1497 Epoch 510/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8544 - d_loss: 0.1624 Epoch 511/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9608 - d_loss: 0.1525 Epoch 512/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4323 - d_loss: 0.1213 Epoch 513/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.5639 - d_loss: 0.1091 Epoch 514/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.5765 - d_loss: 0.1047 Epoch 515/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0967 - d_loss: 0.1355 Epoch 516/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0742 - d_loss: 0.1489 Epoch 517/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0123 - d_loss: 0.1375 Epoch 518/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0835 - d_loss: 0.1568 Epoch 519/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1980 - d_loss: 0.1482 Epoch 520/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9288 - d_loss: 0.1464 Epoch 521/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.8446 - d_loss: 0.1494 Epoch 522/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2086 - d_loss: 0.1242 Epoch 523/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3637 - d_loss: 0.1261 Epoch 524/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.5857 - d_loss: 0.1150 Epoch 525/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3565 - d_loss: 0.1166 Epoch 526/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3999 - d_loss: 0.1129 Epoch 527/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3202 - d_loss: 0.1269 Epoch 528/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2521 - d_loss: 0.1437 Epoch 529/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.7495 - d_loss: 0.1666 Epoch 530/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3421 - d_loss: 0.1267 Epoch 531/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3724 - d_loss: 0.1234 Epoch 532/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9820 - d_loss: 0.1483 Epoch 533/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1390 - d_loss: 0.1401 Epoch 534/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2484 - d_loss: 0.1281 Epoch 535/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0409 - d_loss: 0.1512 Epoch 536/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0701 - d_loss: 0.1472 Epoch 537/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1255 - d_loss: 0.1285 Epoch 538/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3012 - d_loss: 0.1281 Epoch 539/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0562 - d_loss: 0.1382 Epoch 540/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4138 - d_loss: 0.1216 Epoch 541/1000 469/469 [==============================] - 27s 59ms/step - g_loss: 6.3247 - d_loss: 0.1325 Epoch 542/1000 469/469 [==============================] - 27s 59ms/step - g_loss: 6.5423 - d_loss: 0.1245 Epoch 543/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.5191 - d_loss: 0.1286 Epoch 544/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3290 - d_loss: 0.1336 Epoch 545/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.2690 - d_loss: 0.1424 Epoch 546/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.0186 - d_loss: 0.1430 Epoch 547/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.4363 - d_loss: 0.1190 Epoch 548/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.3699 - d_loss: 0.1214 Epoch 549/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 5.9356 - d_loss: 0.1554 Epoch 550/1000 469/469 [==============================] - 28s 59ms/step - g_loss: 6.1943 - d_loss: 0.1387 Epoch 551/1000 469/469 [==============================] - 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53s 112ms/step - g_loss: 6.5029 - d_loss: 0.1256 Epoch 692/1000 469/469 [==============================] - 55s 117ms/step - g_loss: 6.0333 - d_loss: 0.1518 Epoch 693/1000 469/469 [==============================] - 56s 118ms/step - g_loss: 6.3538 - d_loss: 0.1371 Epoch 694/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 6.4485 - d_loss: 0.1163 Epoch 695/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 6.4692 - d_loss: 0.1288 Epoch 696/1000 469/469 [==============================] - 55s 117ms/step - g_loss: 6.2674 - d_loss: 0.1321 Epoch 697/1000 469/469 [==============================] - 80s 171ms/step - g_loss: 6.4202 - d_loss: 0.1407 Epoch 698/1000 469/469 [==============================] - 53s 113ms/step - g_loss: 6.5454 - d_loss: 0.1125 Epoch 699/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 6.5430 - d_loss: 0.1437 Epoch 700/1000 469/469 [==============================] - 59s 126ms/step - g_loss: 6.2991 - d_loss: 0.1413 Epoch 701/1000 469/469 [==============================] - 56s 119ms/step - g_loss: 6.4527 - d_loss: 0.1210 Epoch 702/1000 469/469 [==============================] - 55s 116ms/step - g_loss: 6.4115 - d_loss: 0.1298 Epoch 703/1000 469/469 [==============================] - 58s 124ms/step - g_loss: 6.4241 - d_loss: 0.1217 Epoch 704/1000 469/469 [==============================] - 58s 121ms/step - g_loss: 6.1903 - d_loss: 0.1432 Epoch 705/1000 469/469 [==============================] - 56s 119ms/step - g_loss: 6.3964 - d_loss: 0.1326 Epoch 706/1000 469/469 [==============================] - 56s 120ms/step - g_loss: 6.3376 - d_loss: 0.1362 Epoch 707/1000 469/469 [==============================] - 56s 120ms/step - g_loss: 6.4111 - d_loss: 0.1241 Epoch 708/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 6.0660 - d_loss: 0.1435 Epoch 709/1000 469/469 [==============================] - 55s 118ms/step - g_loss: 6.5711 - d_loss: 0.1259 Epoch 710/1000 469/469 [==============================] - 53s 114ms/step - g_loss: 6.6945 - d_loss: 0.1124 Epoch 711/1000 469/469 [==============================] - 54s 115ms/step - g_loss: 6.6296 - d_loss: 0.1144 Epoch 712/1000 469/469 [==============================] - 65s 138ms/step - g_loss: 6.4187 - d_loss: 0.1440 Epoch 713/1000 469/469 [==============================] - 54s 114ms/step - g_loss: 6.6940 - d_loss: 0.1082 Epoch 714/1000 469/469 [==============================] - 56s 118ms/step - g_loss: 6.5772 - d_loss: 0.1350 Epoch 715/1000 469/469 [==============================] - 58s 123ms/step - g_loss: 6.1541 - d_loss: 0.1336 Epoch 716/1000 469/469 [==============================] - 58s 124ms/step - g_loss: 6.3702 - d_loss: 0.1261 Epoch 717/1000 469/469 [==============================] - 54s 115ms/step - g_loss: 6.5178 - d_loss: 0.1190 Epoch 718/1000 469/469 [==============================] - 54s 115ms/step - g_loss: 6.5825 - d_loss: 0.1302 Epoch 719/1000 469/469 [==============================] - 59s 125ms/step - g_loss: 5.9823 - d_loss: 0.1500 Epoch 720/1000 469/469 [==============================] - 56s 118ms/step - g_loss: 6.5018 - d_loss: 0.1273 Epoch 721/1000 469/469 [==============================] - 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53s 113ms/step - g_loss: 7.4037 - d_loss: 0.1048 Epoch 942/1000 469/469 [==============================] - 53s 113ms/step - g_loss: 7.2418 - d_loss: 0.0975 Epoch 943/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 7.0315 - d_loss: 0.1090 Epoch 944/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 6.9234 - d_loss: 0.1126 Epoch 945/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 6.8743 - d_loss: 0.1093 Epoch 946/1000 469/469 [==============================] - 52s 112ms/step - g_loss: 7.1397 - d_loss: 0.1050 Epoch 947/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.0375 - d_loss: 0.1155 Epoch 948/1000 469/469 [==============================] - 53s 114ms/step - g_loss: 7.0760 - d_loss: 0.1136 Epoch 949/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 7.1938 - d_loss: 0.1063 Epoch 950/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 6.9148 - d_loss: 0.1219 Epoch 951/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.1702 - d_loss: 0.1079 Epoch 952/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.0074 - d_loss: 0.1071 Epoch 953/1000 469/469 [==============================] - 51s 109ms/step - g_loss: 6.7239 - d_loss: 0.1220 Epoch 954/1000 469/469 [==============================] - 53s 113ms/step - g_loss: 7.4008 - d_loss: 0.0905 Epoch 955/1000 469/469 [==============================] - 52s 112ms/step - g_loss: 7.5486 - d_loss: 0.0955 Epoch 956/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 7.7339 - d_loss: 0.0812 Epoch 957/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 7.9632 - d_loss: 0.0834 Epoch 958/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 8.2213 - d_loss: 0.0679 Epoch 959/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 8.1186 - d_loss: 0.0589 Epoch 960/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 8.2530 - d_loss: 0.0690 Epoch 961/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 7.9294 - d_loss: 0.0797 Epoch 962/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.0942 - d_loss: 0.1323 Epoch 963/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 6.9800 - d_loss: 0.1176 Epoch 964/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.3770 - d_loss: 0.0953 Epoch 965/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 6.9825 - d_loss: 0.1214 Epoch 966/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.0203 - d_loss: 0.1314 Epoch 967/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.0080 - d_loss: 0.1315 Epoch 968/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 7.0400 - d_loss: 0.1129 Epoch 969/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 6.8822 - d_loss: 0.1319 Epoch 970/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 7.0475 - d_loss: 0.1184 Epoch 971/1000 469/469 [==============================] - 52s 112ms/step - g_loss: 6.9603 - d_loss: 0.1127 Epoch 972/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 7.5346 - d_loss: 0.0913 Epoch 973/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.4695 - d_loss: 0.0922 Epoch 974/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.3289 - d_loss: 0.0939 Epoch 975/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 7.2466 - d_loss: 0.1199 Epoch 976/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.1586 - d_loss: 0.1145 Epoch 977/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.4502 - d_loss: 0.1129 Epoch 978/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 6.9565 - d_loss: 0.1302 Epoch 979/1000 469/469 [==============================] - 52s 112ms/step - g_loss: 7.3467 - d_loss: 0.1040 Epoch 980/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 6.8989 - d_loss: 0.1158 Epoch 981/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 6.7166 - d_loss: 0.1285 Epoch 982/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.0835 - d_loss: 0.1290 Epoch 983/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 6.9008 - d_loss: 0.1188 Epoch 984/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.1562 - d_loss: 0.1062 Epoch 985/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 6.9842 - d_loss: 0.1197 Epoch 986/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 6.7775 - d_loss: 0.1199 Epoch 987/1000 469/469 [==============================] - 52s 112ms/step - g_loss: 7.1250 - d_loss: 0.0962 Epoch 988/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 6.8780 - d_loss: 0.1162 Epoch 989/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 7.2679 - d_loss: 0.0939 Epoch 990/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.1865 - d_loss: 0.1065 Epoch 991/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 7.3311 - d_loss: 0.1074 Epoch 992/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.1395 - d_loss: 0.1069 Epoch 993/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.3857 - d_loss: 0.1157 Epoch 994/1000 469/469 [==============================] - 53s 112ms/step - g_loss: 7.1133 - d_loss: 0.1117 Epoch 995/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 6.8451 - d_loss: 0.1194 Epoch 996/1000 469/469 [==============================] - 52s 111ms/step - g_loss: 7.1770 - d_loss: 0.1029 Epoch 997/1000 469/469 [==============================] - 52s 110ms/step - g_loss: 7.2165 - d_loss: 0.1159 Epoch 998/1000 469/469 [==============================] - 52s 112ms/step - g_loss: 7.2049 - d_loss: 0.0961 Epoch 999/1000 469/469 [==============================] - 51s 109ms/step - g_loss: 7.3034 - d_loss: 0.1055 Epoch 1000/1000 469/469 [==============================] - 52s 112ms/step - g_loss: 6.7121 - d_loss: 0.1349
tf.keras.models.save_model(generator , "./generator_cgan4.h5")
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
tf.keras.models.save_model(discriminator , "./discriminator_cgan4.h5")
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
# story history object into dataframe
hist_df = pd.DataFrame(con_hist2.history)
# using pandas dataframe to plot out learning curve
with plt.style.context('seaborn'):
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8), tight_layout=True)
hist_df.loc[:, ["d_loss", 'g_loss']].plot(ax=ax1, title=r'Learning Curve of Loss Function CE')
plt.show()
gen = tf.keras.models.load_model("./generator_cgan3.h5")
gen.summary()
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
Model: "generator"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_5 (Dense) (None, 8832) 1227648
leaky_re_lu_15 (LeakyReLU) (None, 8832) 0
reshape_2 (Reshape) (None, 8, 8, 138) 0
conv2d_transpose_4 (Conv2DT (None, 16, 16, 128) 282752
ranspose)
leaky_re_lu_16 (LeakyReLU) (None, 16, 16, 128) 0
conv2d_transpose_5 (Conv2DT (None, 32, 32, 128) 262272
ranspose)
leaky_re_lu_17 (LeakyReLU) (None, 32, 32, 128) 0
conv2d_14 (Conv2D) (None, 32, 32, 3) 24579
=================================================================
Total params: 1,797,251
Trainable params: 1,797,251
Non-trainable params: 0
_________________________________________________________________
# Get the trained generator
trained_gen = cond_gan.generator
num_classes = 10
fig, axs = plt.subplots(num_classes, 10, figsize=(10, num_classes))
for i in range(num_classes):
class_label = keras.utils.to_categorical([i], num_classes)
class_label = tf.cast(class_label, tf.float32)
# Generate 10 random noise vectors
random_noise = tf.random.normal(shape=(10, latent_dim))
# Repeat the class label for each noise vector
class_label = tf.repeat(class_label, repeats=10, axis=0)
# Concatenate the noise and class label
noise_and_label = tf.concat([random_noise, class_label], axis=1)
# Run inference with the generator
fake_images = trained_gen(noise_and_label)
fake_images -= -1
fake_images /= (1 - -1)
# Plot the generated images
for j in range(10):
axs[i, j].imshow(fake_images[j])
axs[i, j].axis("off")
plt.show()
Eliminating fully connected layers on top of convolutional features
using GlobalAveragePooling2D at the last lalyer of Discriminators (Radford et al., 2015) (will hurt convergence speed)
- [x] Add batch normalization for both generator and discriminator
- [x] Add GlobalAveragePooling2D at the last layer of Discriminators
- [x] Data augmentation
Data augmentation generates more data by applying the features below to the dataset
# import dataset
import tensorflow as tf
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
assert x_train.shape == (50000, 32, 32, 3)
assert x_test.shape == (10000, 32, 32, 3)
assert y_train.shape == (50000, 1)
assert y_test.shape == (10000, 1)
# We'll use all the available examples from both the training and test
# sets.
all_digits = np.concatenate([x_train, x_test])
all_labels = np.concatenate([y_train, y_test])
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz 170498071/170498071 [==============================] - 4s 0us/step
from keras.preprocessing.image import ImageDataGenerator
all_digits_rotate = all_digits.copy()
all_labels_rotate = all_labels.copy()
# define data preparation
datagen = ImageDataGenerator(rotation_range=10)
# fit parameters from data
datagen.fit(all_digits_rotate)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(all_digits_rotate, all_labels_rotate, batch_size=9, shuffle=False):
X_batch = X_batch.astype("uint8")
# create a grid of 3x3 images
fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
for i in range(3):
for j in range(3):
ax[i][j].imshow(X_batch[i*3+j])
# show the plot
plt.show()
break
all_digits_rotated = datagen.flow(all_digits_rotate, all_labels_rotate, batch_size=all_digits_rotate.shape[0], shuffle=False).next()
all_digits_rotated = all_digits_rotated[0]
all_digits_flip = all_digits.copy()
all_labels_flip = all_labels.copy()
# define data preparation
datagen = ImageDataGenerator(horizontal_flip=True)
# fit parameters from data
datagen.fit(all_digits_flip)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(all_digits_flip, all_labels_flip, batch_size=9, shuffle=False):
X_batch = X_batch.astype("uint8")
# create a grid of 3x3 images
fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
for i in range(3):
for j in range(3):
ax[i][j].imshow(X_batch[i*3+j])
# show the plot
plt.show()
break
all_digits_flipped = datagen.flow(all_digits_flip, all_labels_flip, batch_size=all_digits_flip.shape[0], shuffle=False).next()
all_digits_flipped = all_digits_flipped[0]
plt.imshow(all_digits_flip[12])
plt.show()
# concat flip shift and rotate
all_digits = np.concatenate((all_digits,all_digits_flipped,all_digits_rotated),axis=0)
all_labels = np.concatenate((all_labels,all_labels_flip,all_labels_rotate),axis=0)
print('total data points after adding all:',all_digits.shape, all_labels.shape)
total data points after adding all: (180000, 32, 32, 3) (180000, 1)
generator_in_channels = latent_dim + num_classes
discriminator_in_channels = num_channels + num_classes
print(generator_in_channels, discriminator_in_channels)
138 13
# Scale the pixel values to [-1, 1] range.
all_digits = all_digits / 127.5 - 1
# one-hot encode the labels.
all_digits = np.reshape(all_digits, (-1, 32, 32, 3))
all_labels = keras.utils.to_categorical(all_labels, 10)
# Create tf.data.Dataset.
dataset = tf.data.Dataset.from_tensor_slices((all_digits, all_labels))
dataset = dataset.shuffle(buffer_size=1024).batch(batch_size)
print(f"Maximal pixel value: {np.max(all_digits)}")
print(f"Minimal pixel value: {np.min(all_digits)}")
print(f"Shape of training images: {all_digits.shape}")
print(f"Shape of training labels: {all_labels.shape}")
Maximal pixel value: 1.0 Minimal pixel value: -1.0 Shape of training images: (180000, 32, 32, 3) Shape of training labels: (180000, 10)
# all_digits = np.reshape(all_digits, (-1, 32, 32, 3))
# all_labels = keras.utils.to_categorical(all_labels, 10)
# # Create tf.data.Dataset.
# dataset = tf.data.Dataset.from_tensor_slices((all_digits,all_labels))
# dataset = dataset.shuffle(buffer_size=1024).batch(batch_size)
# print(f"Maximal pixel value: {np.max(all_digits)}")
# print(f"Minimal pixel value: {np.min(all_digits)}")
# print(f"Shape of training images: {all_digits.shape}")
# print(f"Shape of training labels: {all_labels.shape}")
# print('Dataset Loaded')
# Create the discriminator.
discriminator = keras.Sequential(
[
keras.layers.InputLayer((32, 32, discriminator_in_channels)),
SpectralNormalization(
layers.Conv2D(64, kernel_size=4 , strides=2, padding="same"),
),
layers.BatchNormalization(momentum=0.8),
layers.LeakyReLU(alpha=0.2),
SpectralNormalization(
layers.Conv2D(128, kernel_size=4 , strides=2, padding="same"),
),
layers.BatchNormalization(momentum=0.8),
layers.LeakyReLU(alpha=0.2),
SpectralNormalization(
layers.Conv2D(128, kernel_size=4 , strides=2, padding="same"),
),
layers.BatchNormalization(momentum=0.8),
SpectralNormalization(
layers.Conv2D(256, kernel_size=4 , strides=2, padding="same"),
),
layers.LeakyReLU(alpha=0.2),
layers.GlobalMaxPooling2D(),
layers.Dense(1, activation='sigmoid'),
],
name="discriminator",
)
discriminator.summary()
pip install visualkeras
import visualkeras
visualkeras.layered_view(discriminator, legend=True)
# Create the generator.
generator = keras.Sequential(
[
keras.layers.InputLayer((generator_in_channels,)),
# We want to generate 128 + num_classes coefficients to reshape into a
# 7x7x(128 + num_classes) map.
layers.Dense(2 * 2 * generator_in_channels),
layers.ReLU(),
layers.Reshape((2, 2, generator_in_channels)),
layers.Conv2DTranspose(64, (4, 4), strides=(2, 2), padding="same"),
layers.BatchNormalization(momentum=0.8),
layers.ReLU(),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.BatchNormalization(momentum=0.8),
layers.ReLU(),
layers.Conv2DTranspose(256, (4, 4), strides=(2, 2), padding="same"),
layers.BatchNormalization(momentum=0.8),
layers.ReLU(),
layers.Conv2DTranspose(512, (4, 4), strides=(2, 2), padding="same"),
layers.BatchNormalization(momentum=0.8),
layers.ReLU(),
layers.Conv2D(3, (4, 4), padding="same", activation="tanh"),
],
name="generator",
)
generator.summary()
Model: "generator"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 552) 76728
re_lu (ReLU) (None, 552) 0
reshape (Reshape) (None, 2, 2, 138) 0
conv2d_transpose (Conv2DTra (None, 4, 4, 64) 141376
nspose)
batch_normalization_3 (Batc (None, 4, 4, 64) 256
hNormalization)
re_lu_1 (ReLU) (None, 4, 4, 64) 0
conv2d_transpose_1 (Conv2DT (None, 8, 8, 128) 131200
ranspose)
batch_normalization_4 (Batc (None, 8, 8, 128) 512
hNormalization)
re_lu_2 (ReLU) (None, 8, 8, 128) 0
conv2d_transpose_2 (Conv2DT (None, 16, 16, 256) 524544
ranspose)
batch_normalization_5 (Batc (None, 16, 16, 256) 1024
hNormalization)
re_lu_3 (ReLU) (None, 16, 16, 256) 0
conv2d_transpose_3 (Conv2DT (None, 32, 32, 512) 2097664
ranspose)
batch_normalization_6 (Batc (None, 32, 32, 512) 2048
hNormalization)
re_lu_4 (ReLU) (None, 32, 32, 512) 0
conv2d_4 (Conv2D) (None, 32, 32, 3) 24579
=================================================================
Total params: 2,999,931
Trainable params: 2,998,011
Non-trainable params: 1,920
_________________________________________________________________
visualkeras.layered_view(generator, legend=True)
Added Label smoothing
cond_gan = ConditionalGAN(
discriminator=discriminator, generator=generator, latent_dim=128
)
cond_gan.compile(
d_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
g_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
loss_fn=keras.losses.BinaryCrossentropy(from_logits=False,label_smoothing=0.1),
)
con_hist = cond_gan.fit(dataset, epochs=1000, callbacks=[CONDGANMonitor(num_img=100)])
Output hidden; open in https://colab.research.google.com to view.
BAM RESEARCH PAPER HERE
# story history object into dataframe
hist_df = pd.DataFrame(con_hist.history)
# using pandas dataframe to plot out learning curve
with plt.style.context('seaborn'):
fig, (ax1, ax2) = plt.subplots(1,1, figsize=(16, 8), tight_layout=True)
hist_df.loc[:, ["d_loss", 'g_loss']].plot(ax=ax1, title=r'Learning Curve of Loss Function CE')
plt.show()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-50-2e9c9b5cacaa> in <module> 1 # story history object into dataframe ----> 2 hist_df = pd.DataFrame(con_hist.history) 3 4 # using pandas dataframe to plot out learning curve 5 with plt.style.context('seaborn'): NameError: name 'con_hist' is not defined
# Create the discriminator.
discriminator = keras.Sequential(
[
keras.layers.InputLayer((32, 32, discriminator_in_channels)),
layers.LeakyReLU(alpha=0.2),
SpectralNormalization(
layers.Conv2D(64, kernel_size=4 , strides=2, padding="same"),
),
layers.LeakyReLU(alpha=0.2),
layers.BatchNormalization(momentum=0.8, epsilon=1e-5),
SpectralNormalization(
layers.Conv2D(128, kernel_size=4 , strides=2, padding="same"),
),
layers.LeakyReLU(alpha=0.2),
layers.BatchNormalization(momentum=0.8, epsilon=1e-5),
SpectralNormalization(
layers.Conv2D(128, kernel_size=4 , strides=2, padding="same"),
),
layers.LeakyReLU(alpha=0.2),
layers.BatchNormalization(momentum=0.8, epsilon=1e-5),
layers.GlobalMaxPooling2D(),
layers.Flatten(),
layers.Dropout(0.3),
layers.Dense(1, activation='sigmoid'),
],
name="discriminator",
)
discriminator.summary()
Model: "discriminator"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
leaky_re_lu (LeakyReLU) (None, 32, 32, 13) 0
spectral_normalization (Spe (None, 16, 16, 64) 13440
ctralNormalization)
leaky_re_lu_1 (LeakyReLU) (None, 16, 16, 64) 0
batch_normalization (BatchN (None, 16, 16, 64) 256
ormalization)
spectral_normalization_1 (S (None, 8, 8, 128) 131328
pectralNormalization)
leaky_re_lu_2 (LeakyReLU) (None, 8, 8, 128) 0
batch_normalization_1 (Batc (None, 8, 8, 128) 512
hNormalization)
spectral_normalization_2 (S (None, 4, 4, 128) 262400
pectralNormalization)
leaky_re_lu_3 (LeakyReLU) (None, 4, 4, 128) 0
batch_normalization_2 (Batc (None, 4, 4, 128) 512
hNormalization)
global_max_pooling2d (Globa (None, 128) 0
lMaxPooling2D)
flatten (Flatten) (None, 128) 0
dropout (Dropout) (None, 128) 0
dense (Dense) (None, 1) 129
=================================================================
Total params: 408,577
Trainable params: 407,617
Non-trainable params: 960
_________________________________________________________________
pip install visualkeras
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting visualkeras
Downloading visualkeras-0.0.2-py3-none-any.whl (12 kB)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from visualkeras) (7.1.2)
Collecting aggdraw>=1.3.11
Downloading aggdraw-1.3.15-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (992 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 992.2/992.2 KB 23.4 MB/s eta 0:00:00
Requirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.8/dist-packages (from visualkeras) (1.21.6)
Installing collected packages: aggdraw, visualkeras
Successfully installed aggdraw-1.3.15 visualkeras-0.0.2
import visualkeras
visualkeras.layered_view(discriminator, legend=True)
generator = keras.Sequential(
[
keras.layers.InputLayer((generator_in_channels,)),
# We want to generate 128 + num_classes coefficients to reshape into a
# 7x7x(128 + num_classes) map.
layers.Dense(4 * 4 * generator_in_channels),
layers.ReLU(),
layers.Reshape((4, 4, generator_in_channels)),
layers.Conv2DTranspose(64, (4, 4), strides=(2, 2), padding="same"),
layers.BatchNormalization(momentum=0.8, epsilon=1e-5),
layers.ReLU(),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.BatchNormalization(momentum=0.8, epsilon=1e-5),
layers.ReLU(),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.BatchNormalization(momentum=0.8, epsilon=1e-5),
layers.ReLU(),
layers.Conv2D(3, (4, 4), padding="same", activation="tanh"),
],
name="generator",
)
generator.summary()
Model: "generator"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_2 (Dense) (None, 2208) 306912
re_lu_4 (ReLU) (None, 2208) 0
reshape_1 (Reshape) (None, 4, 4, 138) 0
conv2d_transpose_3 (Conv2DT (None, 8, 8, 64) 141376
ranspose)
batch_normalization_6 (Batc (None, 8, 8, 64) 256
hNormalization)
re_lu_5 (ReLU) (None, 8, 8, 64) 0
conv2d_transpose_4 (Conv2DT (None, 16, 16, 128) 131200
ranspose)
batch_normalization_7 (Batc (None, 16, 16, 128) 512
hNormalization)
re_lu_6 (ReLU) (None, 16, 16, 128) 0
conv2d_transpose_5 (Conv2DT (None, 32, 32, 128) 262272
ranspose)
batch_normalization_8 (Batc (None, 32, 32, 128) 512
hNormalization)
re_lu_7 (ReLU) (None, 32, 32, 128) 0
conv2d_4 (Conv2D) (None, 32, 32, 3) 6147
=================================================================
Total params: 849,187
Trainable params: 848,547
Non-trainable params: 640
_________________________________________________________________
import visualkeras
visualkeras.layered_view(generator, legend=True)
cond_gan = ConditionalGAN(
discriminator=discriminator, generator=generator, latent_dim=128
)
cond_gan.compile(
d_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
g_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
loss_fn=keras.losses.BinaryCrossentropy(from_logits=False,label_smoothing=0.1),
)
con_hist = cond_gan.fit(dataset, epochs=1500, callbacks=[CONDGANMonitor(num_img=100, generator = generator)])
Output hidden; open in https://colab.research.google.com to view.
# story history object into dataframe
hist_df = pd.DataFrame(con_hist.history)
# using pandas dataframe to plot out learning curve
with plt.style.context('seaborn'):
fig, ax = plt.subplots(figsize=(16, 8), tight_layout=True)
hist_df.loc[:, ["d_loss", 'g_loss']].plot(ax=ax, title=r'Learning Curve of Loss Function CE')
plt.show()
import numpy as np
from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.image import resize
from scipy.linalg import sqrtm
import math
from tqdm import tqdm
from keras.utils import to_categorical
class GAN_FID:
def __init__(self, batch_size, latent_dim, sample_size, buffer_size):
# setting Hyperparameters
self.BATCH_SIZE = batch_size
self.LATENT_DIM = latent_dim
self.SAMPLE_SIZE = sample_size
self.BUFFER_SIZE = buffer_size
# setting Constants
self.INCEPTION_SHAPE = (299, 299, 3)
self.INCEPTION = InceptionV3(include_top=False, pooling='avg', input_shape=self.INCEPTION_SHAPE)
self.AUTO = tf.data.AUTOTUNE
# method to set generator and training data
def fit(self, generator, train_data):
# setting generative model and original data used for training
self.GENERATOR = generator
self.train_data = train_data
# Preparing Real Images
trainloader = tf.data.Dataset.from_tensor_slices((self.train_data))
trainloader = (
trainloader
.shuffle(self.BUFFER_SIZE)
.map(self.__resize_and_preprocess, num_parallel_calls=self.AUTO)
.batch(self.BATCH_SIZE, num_parallel_calls=self.AUTO)
.prefetch(self.AUTO)
)
self.trainloader = trainloader
# Generate and prepare Synthetic Images (Fake)
rand_labels = np.random.randint(low=0, high=10, size=self.SAMPLE_SIZE)
rand_labels = to_categorical(rand_labels)
noise = tf.random.normal([self.SAMPLE_SIZE, self.LATENT_DIM])
random_vector_labels = tf.concat([noise, rand_labels], axis=1)
generated_images = self.GENERATOR(random_vector_labels)
genloader = tf.data.Dataset.from_tensor_slices(generated_images)
genloader = (
genloader
.map(self.__resize_and_preprocess, num_parallel_calls=self.AUTO)
.batch(self.BATCH_SIZE, num_parallel_calls=self.AUTO)
.prefetch(self.AUTO)
)
self.genloader = genloader
# prepare embeddings
count = math.ceil(self.SAMPLE_SIZE/self.BATCH_SIZE)
## compute embeddings for real images
print("Computing Real Image Embeddings")
self.real_image_embeddings = self.__compute_embeddings(self.trainloader, count)
## compute embeddings for generated images
print("Computing Generated Image Embeddings")
self.generated_image_embeddings = self.__compute_embeddings(self.genloader, count)
assert self.real_image_embeddings.shape == self.generated_image_embeddings.shape, "Embeddings are not of the same size"
print("Computed Embeddings\tReal Images Embedding Shape: {}\tGenerated Images Embedding Shape".format(
self.real_image_embeddings.shape,
self.generated_image_embeddings.shape
))
# method to produce evaluation results
@tf.autograph.experimental.do_not_convert
def evaluate(self):
# calculate Frechet Inception Distance
fid = self.__calculate_fid(self.real_image_embeddings, self.generated_image_embeddings)
print('The computed FID score is:', fid)
return fid
# method to generate embeddings from inception model
def __compute_embeddings(self, dataloader, count):
image_embeddings = []
for _ in tqdm(range(count)):
images = next(iter(dataloader))
embeddings = self.INCEPTION.predict(images)
image_embeddings.extend(embeddings)
return np.array(image_embeddings)
## STATIC METHODS: these methods knows nothing about the class
# static method to prepare the data before computing Inception Embeddings
@staticmethod
def __resize_and_preprocess(image):
# image *= 255.0 # original image are scaled to [0, 1], scaling back to [0, 255]
# image -= -1
# image /= (1 - (-1))
# image *= 255.
image = tf.image.convert_image_dtype(image, dtype=tf.float32, saturate=True)
# .preprocess_input() expects an image of scale [0, 255]
image = preprocess_input(image)
# inception model expects an image of shape (None, 299, 299, 3)
image = tf.image.resize(image, (299, 299), method='nearest')
return image
# static method to calculate frechet inception distance based on embeddings
@staticmethod
def __calculate_fid(real_embeddings, generated_embeddings):
# calculate mean and covariance statistics
mu1, sigma1 = real_embeddings.mean(axis=0), np.cov(real_embeddings, rowvar=False)
mu2, sigma2 = generated_embeddings.mean(axis=0), np.cov(generated_embeddings, rowvar=False)
# calculate sum squared difference between means
ssdiff = np.sum((mu1 - mu2)**2.0)
# calculate sqrt of product between cov
covmean = sqrtm(sigma1.dot(sigma2))
# check and correct imaginary numbers from sqrt
if np.iscomplexobj(covmean):
covmean = covmean.real
# calculate score
fid = ssdiff + np.trace(sigma1 + sigma2 - 2.0 * covmean)
return fid
%%time
fid_class = GAN_FID(batch_size=512, latent_dim=128, sample_size=10000, buffer_size=1024)
fid_class.fit(generator=generator, train_data=x_test)
fid_score = fid_class.evaluate()
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 87910968/87910968 [==============================] - 0s 0us/step Computing Real Image Embeddings
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Computed Embeddings Real Images Embedding Shape: (10240, 2048) Generated Images Embedding Shape
The computed FID score is: 2.479908285982625 CPU times: user 3min 16s, sys: 46.2 s, total: 4min 2s Wall time: 48.8 s
tf.keras.models.save_model(generator, '/content/drive/MyDrive/Colab Notebooks/DELE_CA2/Models/CGAN/final_SNCGAN_MODEL.h5')
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
gen = tf.keras.models.load_model('/content/drive/MyDrive/Colab Notebooks/DELE_CA2/Models/CGAN/final_SNCGAN_MODEL.h5')
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
# Get the trained generator
num_classes = 10
fig, axs = plt.subplots(num_classes, 10, figsize=(10, num_classes))
for i in range(num_classes):
class_label = keras.utils.to_categorical([i], num_classes)
class_label = tf.cast(class_label, tf.float32)
# Generate 10 random noise vectors
random_noise = tf.random.normal(shape=(10, latent_dim))
# Repeat the class label for each noise vector
class_label = tf.repeat(class_label, repeats=10, axis=0)
# Concatenate the noise and class label
noise_and_label = tf.concat([random_noise, class_label], axis=1)
# Run inference with the generator
fake_images = gen(noise_and_label)
fake_images -= -1
fake_images /= (1 - -1)
# Plot the generated images
for j in range(10):
axs[i, j].imshow(fake_images[j])
axs[i, j].axis("off")
plt.show()